Effects of Individual and Simultaneous Selenium and Iodine Biofortification of Baby-Leaf Lettuce Plants Grown in Two Different Hydroponic Systems

The iodine (I) and selenium (Se) deficiencies affect approximately 30% and 15%, respectively, of the global population. The biofortification of vegetables is a valid way to increase the intake of iodine and selenium through the diet. This study was carried out on baby-leaf lettuce to investigate the effects on plant growth, leaf quality, and leaf I and Se accumulation of adding potassium iodide and sodium selenate, separately and simultaneously, to the nutrient solution in a floating system and aeroponics. The effect of I and Se biofortification on post-harvest quality of lettuce leaves was also evaluated. Our results evidenced that the Se and I treatments increased the content of the two microelements in lettuce leaves without any negative interactions in the plants, when applied either separately or simultaneously. Both hydroponic systems proved to be suitable for producing Se and/or I enriched lettuce. Biofortification with Se was more effective when performed in aeroponics, whereas I biofortification was more effective in the floating system. Quality of leaves during post-harvest storage was not affected by neither of the treatments. Lettuce leaves enriched with 13 µM Se and 5 µMI could be good dietary sources of Se and I without inducing toxic effects in humans.

Allelopathic Efficiency of Plant Extracts to Control Cyanobacteria in Hydroponic Culture

Cyanobacteria rapidly form harmful algal blooms (HABs) that cause serious nutritional imbalances in crop production via hydroponics. Allelopathic extracts from plants can be applied as a solution for ecologically sustainable control of algal blooms. In this study, the effects of 11 aqueous extracts of 10 allelopathic plants in controlling Microcystis aeruginosa were evaluated. Among the extracts, walnut husk and rose leaf extracts exhibited high inhibitory levels for efficient control of algae. High inhibitory levels were achieved owing to large amounts of water-soluble tannins, especially tannic acid. The effective extracts were applied to a hydroponic system cultivated on leafy perilla vegetables. Although the severe doses (IC90) did not guarantee complete algal control due to partial algal regrowth, walnut husk and rose leaf extracts only exerted strong persistent effects on algae control. Persistent algae inhibition contributed to the increase in perilla growth and leaf quality. Rose leaf was potentially a more useful resource for controlling algae in a hydroponic system because the application of rose leaf extract efficiently controlled the algae and was less toxic to perilla growth. In contrast, the treatment of walnut husk extract also controlled algae but inhibited perilla growth with pale green leaves.

Assessment of Physiology and Quality Performances of Cut Foliage Plant (Asparagus plumosus) under Coloured Shade Nets

The investigations were carried out during 2016–17 and 2017–18 under three different coloured shade nets (white, green and red) along with the controlled environment (open condition) at Horticultural Research Station, Mondouri, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India to study the production behavior of Asparagus plumosus, India. The experiments were arranged in Complete Randomized Design (CRD) with four replications and maintained in pots. Similar management practices were followed for all the treatments throughout the growing period. The pooled data of two years revealed that plants grown under the green shade net showed significant improvement in all the vegetative parameters i.e. plant height (109.6 cm), plant spread (70.67 cm), leaf length (33.35 cm), leaf breadth (22.00 cm) and petiole girth (0.80 cm), whereas lowest data observed in the plants maintained without any shade net. The leaf production was also better in the plants raised under green shade net than other treatments. The highest number of leaves per plant and leaf longevity was found in green shade net treatment. The leaf production interval was remarkably lowest in the plants grown under green shade net. The leaf quality was better under green shade net in terms of colour intensity, vase life and chlorophyll content. The green shade net gave overall better performance regarding both physiological and quality parameters and can be suitable for commercial cultivation.

First Report of Paramyrothecium roridum causing leaf blight on water hyacinth (Eichhornia crassipes) in Malaysia

Water hyacinth (Eichhornia crassipes) is a free-floating aquatic plant and is also widely cultivated as an aquatic ornamental plant in Malaysia. In June 2018, a severe foliar disease with typical leaf blight symptoms were observed on leaves of water hyacinth plants (approximately 50%) in waterways adjacent to two rice fields located at Tanjung Karang and Sungai Besar, Selangor province, Malaysia. Symptoms appeared irregular necrotic lesions with concentric rings, later lesions expanded to entire leaves and became blighted. Twenty symptomatic leaves were collected from two sampling locations. Symptomatic leaf tissue was cut into small pieces (5 × 5 mm), surface sterilized with 0.5% sodium hypochlorite (NaOCl) for 2 min, rinsed three times with sterile distilled water, plated on potato dextrose agar (PDA), and incubated at 25 °C with a 12-h light/dark cycle for 7 days. Twenty single-spore isolates were recovered from sampled leaves, all isolates exhibited Paramyrothecium-like morphology and two representative isolates, PR1 and PR2 were used for further studies. Fungal colonies were initially white aerial mycelia with sporodochia bearing olivaceous green conidial masses formed on PDA after 5 days of incubation. Conidiogenous cells were phialidic, hyaline, smooth, straight to slightly curved, 13 to 20 × 1.0 to 1.8 μm and setae were absent. Conidia were aseptate, hyaline to pale green, smooth, cylindrical to ellipsoidal with rounded ends, and measured 5.8 to 8.0 μm × 1.8 to 2.2 μm (n=50). These morphological characteristics were consistent with the description of Paramyrothecium roridum (Tode) L. Lombard & Crous (Lombard et al. 2016). Total genomic DNA of the isolates was extracted from fresh mycelium using DNeasy Plant Mini kit (Qiagen, USA). The internal transcribed spacer (ITS) and calmodulin (cmdA) gene regions were amplified using the ITS5/ITS4 (White et al.1990) and CAL-228F/CAL2Rd primer sets (Carbone and Kohn 1999; Groenewald et al., 2013), respectively. BLASTn analysis showed that the ITS and cmdA sequences of the isolates were 100% identity with Paramyrothecium roridum ex-epitype strain CBS 357.89 (GenBank accession nos. KU846300 and KU846270), respectively. The resulting sequences were deposited in GenBank (ITS: Accession nos. MW850370, MW850371; cmdA Accession nos. MW854363, MW854364). Pathogenicity tests of the two isolates were performed by spray inoculation on healthy leaves of each five potted water hyacinth plants using a 3-ml conidial suspension (1 × 106 conidia/ml) produced on 7-day-old PDA cultures incubated at 25 °C with a 12-h light/dark cycle. Five potted water hyacinth plants inoculated with sterile water served as controls. Inoculated plants were covered with plastic bags for 48 h to maintain high humidity and kept in a growth chamber for 2 weeks at 25 ± 1°C, 95% relative humidity and a 12-h light/dark period. The experiment was repeated twice. Eight days post-inoculation, symptoms on inoculated leaves developed necrotic brown lesions similar to those observed in the field, while control leaves remained asymptomatic. After 2 weeks of inoculation, lesions enlarged into severe blighting until all leaves died. Paramyrothecium roridum was re-isolated from randomly selected symptomatic tissues and verified by morphology and sequencing of ITS (MZ675387, MZ706462) and cmdA (MZ686706, MZ712041) loci, confirming Koch’s postulates. The fungus was not re-isolated from non-inoculated control plants. Pa. roridum is distributed on a wide range of plants (Farr and Rossman 2021) and has been reported to cause leaf spot of water hyacinth in Nigeria (Okunowo et al. 2013) and Sri Lanka (Adikaram and Yakandawala 2020). To our knowledge, this is the first report of Pa. roridum causing leaf blight of water hyacinth in Malaysia. This disease is an emerging threat to water hyacinth and it reduces the leaf quality, therefore, appropriate management should be developed to control this disease.

Balanced Fertilization for Improved Nutrient use Efficiency and Mulberry Productivity

Aim: To study the effects of balanced fertilization in improving leaf yield, quality and nutrient use efficiency in mulberry. Study Design: Experiment was conducted in randomized block design (RBD) consisting of 7 treatments in 3 replications. Place and Duration of Study: The present study was conducted at Central Sericultural Research and Training Institute, Berhampore ((Latitude 24º05ʹN & Longitude 88º15ʹE; 18 m > MSL), West Bengal, India in two seasons during 2018 (July-August; September-October). Methodology: Existing S-1635 mulberry plantation (10year-old; 60 cm × 60 cm spacing; net plot area: 32 m2) was utilized. Experiments were conducted in two seasons (July-August, 2018; September-October, 2018) in randomized block design (RBD) consisting of 7 treatments in 3 replications. The treatments in this study included, T1: 100% N-P2O5-K2O+S+Zn with Urea-SSP-10:26:26 + Bentonite Sulphur (8 kg ha-1) + Zinc Sulphate (1 kg ha-1) as BF; T2: 75% BF; T3: 100% N-P2O5-K2O alone (Urea-SSP-10:26:26); T4: 75% N-P2O5-K2O alone (Urea-SSP-10:26:26); T5: 100% N-P2O5-K2O alone (Urea-DAP-MOP); T6: 100% N-P2O5-K2O alone (Urea-SSP-MOP) as farmers′ practice (FP); T7: Nutrients omission plot (Control). The recommended fertilizer dose (100%) for irrigated mulberry production in the Eastern region is N-P2O5-K2O=67-36-22 kg ha-1 crop-1. All the fertilizers were applied in two equal splits on 15th and 30th day after pruning. Results: This study reveals the importance of balanced fertilization of mulberry with N, P, K, S and Zn for sustainable productivity, which is reflected by the maximum values for leaf yield attributes, chlorophyll content and leaf yield. Further, mulberry leaf quality (in terms of total soluble protein and total soluble sugar) significantly improved with balanced fertilization. Higher PFP and AUE were also recorded with fertilization with compound/complex fertilizers than with straight fertilizers; but the balanced fertilization exhibited remarkable enhancement. Conclusion: The results prove that balanced fertilization of N-P2O5-K2O @ 67-36-22 kg ha-1 (Urea-SSP-10:26:26) with Bentonite sulphur (8 kg ha-1) and zinc sulphate (1 kg ha-1) were effective in improving mulberry productivity through enhanced nutrient use efficiency. This could be useful for realizing maximum productivity in mulberry as an efficient nutrient management strategy in mulberry cultivation.

Comparison of the Non-Invasive Monitoring of Fresh-Cut Lettuce Condition with Imaging Reflectance Hyperspectrometer and Imaging PAM-Fluorimeter

We compared two approaches to non-invasive proximal sensing of the early changes in fresh-cut lettuce leaf quality: hyperspectral imaging and imaging of variable chlorophyll fluorescence contained in the leaves. The estimations made by the imaging techniques were confronted with the quality assessments made by traditional biochemical assays (i.e., relative water content and foliar pigment (chlorophyll and carotenoid) composition. The hyperspectral imaging-based approach provided the highest sensitivity to the decline of fresh-cut lettuce leaf quality taking place within 24 h from cutting. Using of the imaging pulse-amplitude modulated PAM chlorophyll fluorometer was complicated by (i) weak correlation of the spatial distribution pattern of the Qy parameter with the actual physiological condition of the plant object and (ii) its high degree of heterogeneity. Accordingly, the imaging PAM-based approach was sensitive only to the manifestations of leaf quality degradation at advanced stages of the process. Sealing the leaves in polyethylene bags slowed down the leaf quality degradation at the initial stages (<three days) but promoted its rate at more advanced stages, likely due to build-up of ethylene in the bags. An approach was developed to the processing of hyperspectral data for non-invasive monitoring of the lettuce leaves with a potential for implementation in greenhouses and packing lines.

Klasifikasi Kualitas Mutu Daun Gambir Ladang Rakyat Menggunakan Metode Convolutional Neural Network

Indonesia is one of the countries which have the best Gambier quality in the world. Those are a few areas in Indonesia which have best gambier quality such as Aceh, Riau, North Sumatera, Bengkulu, South Sumatera and West Sumatra. Kabupaten 50 Kota is one of the regencies in west Sumatra that supplies gambier in Indonesia. The gambier leaf selection is mostly done by manual inspection or conventional method. The leaf color, thickness and structure are the important parameters in selecting gambier leaf quality. Farmers usually classify the quality of gambier leaves into good and bad. Computer Vision can help farmers to classify gambier leaves automatically. To realize this proposed method, gambier leaves are collected to create a dataset for training and testing processes. The gambier image leaves is captured by using DLSR camera at Kabupaten 50 Koto manually. 60 images were collected in this research which separated into 30 images with good and 30 images with bad quality. Furthermore, the gambier leaves image is processed by using digital image processing and coded by using python programming language. Both TensorFlow and Keras were implemented as frameworks in this research. To get a faster processing time, Ubuntu 18.04 Linux is selected as an operating system. Convolutional Neural Network (CNN) is the basis of image classification and object detection. In this research, the miniVGGNet architecture was used to perform the model creation. A quantity of dataset images was increased by applying data augmentation methods. The result of image augmentation for good quality gambier produced 3000 images. The same method was applied to poor quality images, the same results were obtained as many as 3000 images, with a total of 6000 images. The classification of gambier leaves produced by the Convolutional Neural Network method using miniVGGNet architecture obtained an accuracy rate of 0.979 or 98%. This method can be used to classify the quality of Gambier leaves very well.

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

Vegetation Type and Soil Moisture Drive Variations in Leaf Litter Decomposition Following Secondary Forest Succession

Soil moisture was an important factor affecting litter decomposition. However, less attention has been given to the complete succession ecosystem after farmland abandonment. To better understand the effect of moisture on leaf litter decomposition after farmland abandonment, in this study, we used three water gradients (10%, 25% and 50%) of field moisture capacity for succession vegetation. Furthermore, we used the typical species leaf litter decomposition of four succession stages—grassland (GL), shrubland (SL), pioneer forest (PF), and climax forest (CF) from the Loess Plateau of China. The results showed that leaves decomposition rate exhibited an increasing pattern with increasing moisture contents. The decomposition trend was shown as GL > SL > PF > CF. During the decomposition process, the leaf carbon concentration (LC) and leaf nitrogen concentration (LN) changed, but non-significantly. The effects of LC, LN, and LC: LN on leaf decomposition varied with vegetation type. Soil properties such as NH4+, NO3−, dissolved organic nitrogen (DON), and leaf quality parameters such as leaf cellulose, lignin, lignin: LN, and lignin: LC played an important role in driving leaf litter decomposition. Overall, the results provide evidence that litter decomposition in secondary forest succession system was linked to leaf and soil nutrient dynamics, and was limited by soil moisture.